Lactate Correlation Paper: Nanostring Analysis of All Data

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Section 1: Expression analysis and model generation using original 10 samples.

Figure 1: Barplot of correlation between biomarker expression and lactate levels
Figure 2: Heatmap of scaled biomarker expression data
Figure 3: Correlation between mean scaled biomarker expression and lactate
Figure 4: Multiple linear regression predictive model
Figure 5: Checking model conditions
Figure 6: Correlation between the models predicted lactate levels and donor metrics

Section 2: Incorporating additional samples into the predictive model.

Figure 7: Incorporating the additional samples into the established model
Figure 8: Correlation between the models predicted lactate levels for all samples and donor metrics
Figure 9: Expression levels of all biomarkers with lactate for all samples
Figure 10: Heatmap of scaled biomarker expression data for all samples
Figure 11: Correlation between mean scaled biomarker expression and lactate for all samples
Figure 12: PCA using scaled biomarker data for all samples
Figure 13: PCA using scaled biomarker data for the original 10 samples only
Figure 14: Multiple Linear Regression Predictive Model generated using all samples
Figure 15: Checking the new model conditions
Figure 16: Correlation between the new models predicted lactate levels for all samples and donor metrics

Section 3: Additional analyses.

Figure 17: Generating a model using genes that do not correlate with lactate.
Figure 18: Correlation between gene expression and 3 hour lactate levels for DBD livers.
Figure 19: Correlation between gene expression and 3 hour lactate levels for DBD and DCD livers.
Figure 20: Correlation of genes that correlate with lactate in DBD livers in DBD(NS), DCD(NS), and DCD(RNAseq).
Figure 21: Fold change between adequate and low functioning livers for genes that correlate with lactate in DBD livers in DBD(NS), DCD(NS), and DCD(RNAseq).
Figure 22: Expression levels for genes that correlate with lactate in DBD livers in DBD(NS), DCD(NS), and DCD(RNAseq).

Summary of Section 3.

The model using non-correlating genes is not as accurate as the model using correlating genes (Fig17).
DBD livers do have genes that correlate with lactate in the nanostring data (Fig18 and Fig20).
Combined data for DBD and DCD does not have correlating genes in the nanostring data (Fig19)
One of the DBD correlating genes (SERF2) has sufficient fold change (Fig21) and expression (Fig22) to act as biomarkers.
There are significant discrepencies observed between the nanostring and RNAseq data for several of these genes in correlation (fig20) and fold change (fig21) of DCD samples.
One of the genes (NRF1) correlates (fig20), has sufficient fold change (fig21) and expression in the nanostring data but not in the RNAseq data for the DCD samples.
Generating a model using the original biomarker data from DCD samples and incorporating fwit does mildly improve the model.

Section 1: Expression analysis and model generation using nanostring data.

Expression levels of putative biomarkers identified with RNAseq were validated using a custom nanstring panel and tested for conserved correlation with 3 hour lactate levels. Genes that met the biomarker criteria in both analyses were then used to generate a multiple linear regression predictive model.

Figure 1: The correlation of sufficiently expressed candidate biomarkers with 3 hour lactate levels. Genes needed to be above the negative control threshold in at least 8/10 samples to be considered. Dashed lines represent the 80% correlation threshold.

Figure 2: Heatmap with biomarker expression data scaled by gene/row and hierarchally clustered by sample/column. Functional assessment based on the 3 hour lactate levels is labeled above the columns. Data for the 7 biomarkers with sufficient expression and correlation with lactate were used from all 10 of the original liver samples.

Mean relative expression of the geneset correlated with 3 hour lactate levels. Light grey lines are the relative expression levels of each biomarker. The black line is the mean of the relative expression levels with error bars representing the variance. The dashed red line is the lactate level observed for each sample at 3 hours of perfusion. The Pearson Correlation coefficient and p-value for the correlation between mean relative expression and lactate are listed in the legend.

Figure 3: Mean relative expression of the geneset correlated with 3 hour lactate levels. Light grey lines are the relative expression levels of each biomarker. The black line is the mean of the relative expression levels with error bars representing the variance. The dashed red line is the lactate level observed for each sample at 3 hours of perfusion. The Pearson Correlation coefficient and p-value for the correlation between mean relative expression and lactate are listed in the legend.

## 
## Call:
## lm(formula = LacData ~ EPHX1 + TKT + GPX2 + JUN + CYP2B6 + GSTA1 + 
##     GSTA2, data = mlm.data)
## 
## Residuals:
##       FV2       LV2       LV3       FV3       LV1       FN1       FN2       LN1 
## -1.988045 -0.512516 -0.786381 -0.364826  2.926899  0.921534 -0.290795 -0.002211 
##       FN3       LN3 
## -0.009832  0.106172 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.402e+00  2.639e+00  -0.910    0.459
## EPHX1       -1.030e-04  1.590e-03  -0.065    0.954
## TKT          1.063e-02  3.030e-02   0.351    0.759
## GPX2        -8.466e-04  1.971e-03  -0.429    0.709
## JUN          1.716e-02  3.227e-02   0.532    0.648
## CYP2B6      -6.794e-05  3.519e-04  -0.193    0.865
## GSTA1        5.072e-05  1.724e-04   0.294    0.796
## GSTA2        6.202e-04  1.113e-03   0.557    0.634
## 
## Residual standard error: 2.691 on 2 degrees of freedom
## Multiple R-squared:  0.9721, Adjusted R-squared:  0.8744 
## F-statistic: 9.949 on 7 and 2 DF,  p-value: 0.09434

Figure 4: Generating a Multiple Linear Regression Predictive Model and applying it to the observed expression levels. The model is generated by incorporating expression data from the 7 biomarker genes across the 10 original sample livers and calculating a functional relationship to the lactate levels observed in their corresponding sample. The resulting intercept and coefficients for individual gene expression levels can be used to predict lactate levels for a sample. The plot displays the relationship between the levels predicted by the model when gene expression levels observed in the original 10 sample livers are used and the actual observed levels for those livers. The dashed line represents a 1:1 relationship between obersved and predicted lactate levels.

Checking model conditions. These figures are used to check the quality of the model by testing specific statistical parameters that are required for reliable application.

Figure 5: Checking model conditions. These figures are used to check the quality of the model by testing specific statistical parameters that are required for reliable application.

Figure 6: The predicted lactate levels generated by the model were tested for correlation with the known donor metrics.

Section 2: Incorporating additional samples into the model.

In order to validate the capabilities of these genes to serve as biomarkers, expression was measured in 7 additional livers with the custom nanostring panel.

Figure 7: Incorporating the additional samples into the established model

Figure 8: Correlation between the models predicted lactate levels and donor metrics

Figure 9: Expression levels of the 23 putative biomarkers observed in all 17 samples correlated with 3 hour lactate levels. The black lines represent 80% correlation and the red represents 60% correlation

Figure 10: Heatmap with data scaled by gene/row and clustered by sample/column. Functional data and DCD/DBD status is labeled above the columns

Mean relative expression of the geneset using all samples correlated with 3 hour lactate levels. Light grey lines are the relative expression levels of each biomarker. The black line is the mean of the relative expression levels with error bars representing the variance. The dashed red line is the lactate level observed for each sample at 3 hours of perfusion. The Pearson Correlation coefficient and p-value for the correlation between mean relative expression and lactate are listed in the legend.

Figure 11: Mean relative expression of the geneset using all samples correlated with 3 hour lactate levels. Light grey lines are the relative expression levels of each biomarker. The black line is the mean of the relative expression levels with error bars representing the variance. The dashed red line is the lactate level observed for each sample at 3 hours of perfusion. The Pearson Correlation coefficient and p-value for the correlation between mean relative expression and lactate are listed in the legend.

Figure 12: PCA using scaled data for all 17 samples

Figure 13: PCA using scaled data for the original 10 samples only

## 
## Call:
## lm(formula = all.LacData ~ EPHX1 + TKT + GPX2 + JUN + CYP2B6 + 
##     GSTA1 + GSTA2, data = mlm.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4829 -2.0591 -0.6445  1.4872  8.9090 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.8797389  2.5375224  -0.347   0.7368  
## EPHX1       -0.0019644  0.0009802  -2.004   0.0760 .
## TKT          0.0271152  0.0101518   2.671   0.0256 *
## GPX2         0.0001518  0.0011427   0.133   0.8973  
## JUN          0.0008919  0.0029752   0.300   0.7712  
## CYP2B6       0.0001317  0.0002912   0.452   0.6618  
## GSTA1        0.0001865  0.0001693   1.101   0.2993  
## GSTA2        0.0010973  0.0006848   1.602   0.1435  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.208 on 9 degrees of freedom
## Multiple R-squared:   0.82,  Adjusted R-squared:  0.6799 
## F-statistic: 5.856 on 7 and 9 DF,  p-value: 0.008688

Figure 14: Generating a Multiple Linear Regression Predictive Model using all samples and applying it to the observed expression levels of all 17 samples

Figure 15: Checking the new model conditions

Figure 16: Correlation between the new models predicted lactate levels and donor metrics

Section 3: Additional analyses.

##   HSP90B1      TFAM       ALB      BCL2    COX6B1     COX17      MCL1 
## 0.0853985 0.3698896 0.1814015 0.2063185 0.3290794 0.1212330 0.4920644

## 
## Call:
## lm(formula = LacData ~ gene1 + gene2 + gene3 + gene4 + gene5 + 
##     gene6 + gene7, data = mlm.data)
## 
## Residuals:
##     FV2     LV2     LV3     FV3     LV1     FN1     FN2     LN1     FN3     LN3 
## -2.6489 -9.0340 -2.3194 -3.4053  2.2324 -0.9814  0.8099 -1.9560 11.0976  6.2053 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.535e+00  2.416e+01  -0.188    0.868
## gene1        9.516e-04  4.406e-03   0.216    0.849
## gene2        1.457e-01  2.110e-01   0.690    0.561
## gene3       -1.658e-05  1.608e-04  -0.103    0.927
## gene4       -2.814e-01  4.838e-01  -0.582    0.620
## gene5       -1.146e-02  3.094e-02  -0.370    0.747
## gene6        2.330e-03  4.199e-02   0.055    0.961
## gene7        3.185e-02  6.646e-02   0.479    0.679
## 
## Residual standard error: 11.78 on 2 degrees of freedom
## Multiple R-squared:  0.4646, Adjusted R-squared:  -1.409 
## F-statistic: 0.2479 on 7 and 2 DF,  p-value: 0.9317

Figure 17: Generating a model using genes that do not correlate with lactate. Uses a random set of 7 genes from the nanostring data that have sufficient expression and a positive correlation value less than .5.

Figure 18: The correlation of sufficiently expressed genes detected in the nanostring custom panel with 3 hour lactate levels in DBD livers. Genes needed to be above the negative control threshold in at least 8/10 samples to be considered. Dashed lines represent the 80% correlation threshold.

Figure 19: The correlation of sufficiently expressed genes detected in the nanostring custom panel with 3 hour lactate levels in DBD and DCD livers. Genes needed to be above the negative control threshold in at least 8/10 samples to be considered. Dashed lines represent the 80% correlation threshold.

Figure 20: Showing only those with at least 80% correlation in the DBD livers. Note: NRF1 was not used in the original analysis of DCD livers since there was a low correlation between the Lactade data and RNAseq data.

Figure 21: Showing the fold change between adequate and low functioning livers for those genes with at least 80% correlation. Note: NRF1 was not used in the original analysis of DCD livers since there was a low correlation between the Lactade data and RNAseq data.

Figure 22: Showing the expression leves for those genes with at least 80% correlation.

## 
## Call:
## lm(formula = all.LacData ~ EPHX1 + TKT + GPX2 + JUN + CYP2B6 + 
##     GSTA1 + GSTA2 + fwit, data = mlm.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9600 -2.2272 -0.7964  1.6400  8.7994 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.4960950  3.9506282   0.126   0.9032  
## EPHX1       -0.0019193  0.0010300  -1.863   0.0994 .
## TKT          0.0293559  0.0116414   2.522   0.0357 *
## GPX2        -0.0001388  0.0013457  -0.103   0.9204  
## JUN         -0.0006743  0.0045585  -0.148   0.8861  
## CYP2B6       0.0001509  0.0003074   0.491   0.6367  
## GSTA1        0.0002121  0.0001853   1.144   0.2856  
## GSTA2        0.0010787  0.0007176   1.503   0.1712  
## fwit        -0.1753180  0.3727847  -0.470   0.6507  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.402 on 8 degrees of freedom
## Multiple R-squared:  0.8248, Adjusted R-squared:  0.6496 
## F-statistic: 4.708 on 8 and 8 DF,  p-value: 0.02103

Figure 23: Generating a model using the original biomarkers and incorporating wit.

Lactate Correlation Paper: Nanostring Analysis of All Data

John Santiago

2022-03-23

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Table of Contents

Section 1: Expression analysis and model generation using original 10 samples.

Figure 1: Barplot of correlation between biomarker expression and lactate levels

Figure 2: Heatmap of scaled biomarker expression data

Figure 3: Correlation between mean scaled biomarker expression and lactate

Figure 4: Multiple linear regression predictive model

Figure 5: Checking model conditions

Figure 6: Correlation between the models predicted lactate levels and donor metrics

Section 2: Incorporating additional samples into the predictive model.

Figure 7: Incorporating the additional samples into the established model

Figure 8: Correlation between the models predicted lactate levels for all samples and donor metrics

Figure 9: Expression levels of all biomarkers with lactate for all samples

Figure 10: Heatmap of scaled biomarker expression data for all samples

Figure 11: Correlation between mean scaled biomarker expression and lactate for all samples

Figure 12: PCA using scaled biomarker data for all samples

Figure 13: PCA using scaled biomarker data for the original 10 samples only

Figure 14: Multiple Linear Regression Predictive Model generated using all samples

Figure 15: Checking the new model conditions

Figure 16: Correlation between the new models predicted lactate levels for all samples and donor metrics

Section 3: Additional analyses.

Figure 17: Generating a model using genes that do not correlate with lactate.

Figure 18: Correlation between gene expression and 3 hour lactate levels for DBD livers.

Figure 19: Correlation between gene expression and 3 hour lactate levels for DBD and DCD livers.

Figure 20: Correlation of genes that correlate with lactate in DBD livers in DBD(NS), DCD(NS), and DCD(RNAseq).

Figure 21: Fold change between adequate and low functioning livers for genes that correlate with lactate in DBD livers in DBD(NS), DCD(NS), and DCD(RNAseq).

Figure 22: Expression levels for genes that correlate with lactate in DBD livers in DBD(NS), DCD(NS), and DCD(RNAseq).

Summary of Section 3.

The model using non-correlating genes is not as accurate as the model using correlating genes (Fig17).

DBD livers do have genes that correlate with lactate in the nanostring data (Fig18 and Fig20).

Combined data for DBD and DCD does not have correlating genes in the nanostring data (Fig19)

One of the DBD correlating genes (SERF2) has sufficient fold change (Fig21) and expression (Fig22) to act as biomarkers.

There are significant discrepencies observed between the nanostring and RNAseq data for several of these genes in correlation (fig20) and fold change (fig21) of DCD samples.

One of the genes (NRF1) correlates (fig20), has sufficient fold change (fig21) and expression in the nanostring data but not in the RNAseq data for the DCD samples.

Generating a model using the original biomarker data from DCD samples and incorporating fwit does mildly improve the model.

Section 1: Expression analysis and model generation using nanostring data.

Section 2: Incorporating additional samples into the model.

In order to validate the capabilities of these genes to serve as biomarkers, expression was measured in 7 additional livers with the custom nanostring panel.

Section 3: Additional analyses.